gemini-2.5-flash
Model Overview
Gemini 2.5 models are capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.
How to Make a Call
API Schema
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
512The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
512If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseHow many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
An object specifying the format that the model must output.
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.
Whether to enable parallel function calling during tool use.
POST /v1/chat/completions HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 554
{
"model": "google/gemini-2.5-flash",
"messages": [
{
"role": "user",
"content": "text",
"name": "text"
}
],
"max_completion_tokens": 512,
"max_tokens": 512,
"stream": false,
"stream_options": {
"include_usage": true
},
"n": 1,
"temperature": 1,
"top_p": 1,
"stop": "text",
"frequency_penalty": 1,
"prediction": {
"type": "content",
"content": "text"
},
"presence_penalty": 1,
"seed": 1,
"response_format": {
"type": "text"
},
"tools": [
{
"type": "function",
"function": {
"description": "text",
"name": "text",
"parameters": null,
"strict": true,
"required": [
"text"
]
}
}
],
"tool_choice": "none",
"parallel_tool_calls": true
}No content
Code Example
A common issue when using reasoning-capable models via API is receiving an empty string in the content field—meaning the model did not return the expected text, yet no error was thrown.
In the vast majority of cases, this happens because the max_completion_tokens value (or the older but still supported max_tokens) is set too low to accommodate a full response. Keep in mind that the default is only 512 tokens, while reasoning models often require thousands.
Pay attention to the finish_reason field in the response. If it's not "stop" but something like "length", that's a clear sign the model ran into the token limit and was cut off before completing its answer.
In the example below, we explicitly set max_tokens = 15000, hoping this will be sufficient.
import requests
import json # for getting a structured output with indentation
response = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={
"Content-Type":"application/json",
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"google/gemini-2.5-flash",
"messages":[
{
"role":"user",
# Insert your question for the model here:
"content":"Hi! What do you think about mankind?"
}
]
"max_tokens":15000,
}
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))Last updated
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